Update app.py

app.py

@@ -2,29 +2,47 @@ import gradio as gr
 import joblib
 import numpy as np
 
-# Load the saved model
-model = joblib.load('best_model.pkl')
-
-# Load the saved pipeline (which includes the scaler and the model)
-pipeline = joblib.load('best_pipeline.pkl')
-
-# Define the prediction function
-def predict(input1, input2, input3, input4, input5,input6):
-    # Create a numpy array from the inputs
-    inputs = np.array([input1, input2, input3, input4, input5,input6]).reshape(1, -1)
-    
-    # Transform the inputs using the scaler
-    inputs_scaled = pipeline.transform(inputs)
-    
-    # Make the prediction
-    prediction = model.predict(inputs_scaled)
-    
-    # Return the prediction and a description
+# Load the saved models and pipelines
+anxiety_model = joblib.load('Anxiety_best_model.pkl')
+anxiety_pipeline = joblib.load('Anxiety_best_pipeline.pkl')
+
+depression_model = joblib.load('Depression_best_model.pkl')
+depression_pipeline = joblib.load('Depression_best_pipeline.pkl')
+
+insomnia_model = joblib.load('Insomnia_best_model.pkl')
+insomnia_pipeline = joblib.load('Insomnia_best_pipeline.pkl')
+
+ocd_model = joblib.load('OCD_best_model.pkl')
+ocd_pipeline = joblib.load('OCD_best_pipeline.pkl')
+
+# Define the prediction functions
+def Anxiety_predict(input1, input2, input3, input4, input5, input6):
+    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
+    inputs_scaled = anxiety_pipeline.transform(inputs)
+    prediction = anxiety_model.predict(inputs_scaled)
     return prediction[0], f"The predicted value is {prediction[0]:.2f}"
 
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=predict,  # Function to call
+def Depression_predict(input1, input2, input3, input4, input5, input6):
+    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
+    inputs_scaled = depression_pipeline.transform(inputs)
+    prediction = depression_model.predict(inputs_scaled)
+    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+
+def Insomnia_predict(input1, input2, input3, input4, input5, input6):
+    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
+    inputs_scaled = insomnia_pipeline.transform(inputs)
+    prediction = insomnia_model.predict(inputs_scaled)
+    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+
+def OCD_predict(input1, input2, input3, input4, input5, input6):
+    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
+    inputs_scaled = ocd_pipeline.transform(inputs)
+    prediction = ocd_model.predict(inputs_scaled)
+    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+
+# Define the Gradio interfaces
+anxiety_iface = gr.Interface(
+    fn=Anxiety_predict,
     inputs=[
         gr.Number(label="Age"),
         gr.Number(label="Hours per day"),
@@ -37,11 +55,80 @@ iface = gr.Interface(
         gr.Number(label="Predicted Value"),
         gr.Textbox(label="Prediction Description")
     ],
-    title="Music & Mental Health Predictor",
-    description="""This Model has been trained on this <a href="https://www.kaggle.com/datasets/catherinerasgaitis/mxmh-survey-results">Dataset</a>.""",
+    title="Music & Mental Health Predictor - Anxiety",
+    description="Enter 5 numeric values to get a prediction.",
+    theme=gr.themes.Soft(),
+    examples=[[18, 3, 0, 1, 0, 156]]
+)
+depression_iface = gr.Interface(
+    fn=Depression_predict,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="Hours per day"),
+        gr.Number(label="Insomnia"),
+        gr.Number(label="Anxiety"),
+        gr.Number(label="OCD"),
+        gr.Number(label="BPM")
+    ],
+    outputs=[
+        gr.Number(label="Predicted Value"),
+        gr.Textbox(label="Prediction Description")
+    ],
+    title="Music & Mental Health Predictor - Depression",
+    description="Enter 5 numeric values to get a prediction.",
     theme=gr.themes.Soft(),
-    examples=[[18,3,0,1,0,156]]
+    examples=[[18, 3, 0, 1, 0, 156]]
 )
+insomnia_iface = gr.Interface(
+    fn=Insomnia_predict,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="Hours per day"),
+        gr.Number(label="Depression"),
+        gr.Number(label="Anxiety"),
+        gr.Number(label="OCD"),
+        gr.Number(label="BPM")
+    ],
+    outputs=[
+        gr.Number(label="Predicted Value"),
+        gr.Textbox(label="Prediction Description")
+    ],
+    title="Music & Mental Health Predictor - Insomnia",
+    description="Enter 5 numeric values to get a prediction.",
+    theme=gr.themes.Soft(),
+    examples=[[18, 3, 0, 1, 0, 156]]
+)
+
+ocd_iface = gr.Interface(
+    fn=OCD_predict,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="Hours per day"),
+        gr.Number(label="Insomnia"),
+        gr.Number(label="Anxiety"),
+        gr.Number(label="Depression"),
+        gr.Number(label="BPM")
+    ],
+    outputs=[
+        gr.Number(label="Predicted Value"),
+        gr.Textbox(label="Prediction Description")
+    ],
+    title="Music & Mental Health Predictor - OCD",
+    description="Enter 5 numeric values to get a prediction.",
+    theme=gr.themes.Soft(),
+    examples=[[18, 3, 0, 1, 0, 156]]
+)
+
+# Create a tabbed interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Tab("Anxiety"):
+        anxiety_iface.render()
+    with gr.Tab("Depression"):
+        depression_iface.render()
+    with gr.Tab("Insomnia"):
+        insomnia_iface.render()
+    with gr.Tab("OCD"):
+        ocd_iface.render()
 
 # Launch the interface
-iface.launch(debug=True)
+demo.launch(debug=True)